Compensation for Moral Damages in Investor-State Arbitration Disputes
Bibliographic record
Abstract
This article examines the issue of monetary compensation awarded by arbitral tribunals for moral damages suffered by foreign investors in the context of investor–state arbitration. It examines the nature and the function of moral damages in international investment law as well as several controversial issues, including the proper form of reparation to remediate moral damages suffered by a state, whether proof of malicious intent is a necessary condition for a tribunal to award compensation and whether compensation should be limited to cases involving “egregious” or grave treaty violations. The article argues that particularly condemnable governmental actions toward foreign investors will have a bearing on the actual quantification of the amount of compensation to be awarded for moral damages. The goal is not only to remediate the actual damage suffered but also to send a “clear message” to the host state. “La réparation morale contient un élément de châtiment” Hersch Lauterpacht
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".